However, finding this homography can be challenging because neighboring cameras can have different angles of view, leading to a perspective distortion between the images captured by them. Given a pair of neighboring nodes \(\nu _i, \nu _j\) , we employ a procedure that starts with finding the SIFT [1]} key-points and feature descriptors of the images \(i, j\)  captured by the two nodes. Then, we match the two sets of feature descriptors performing the David Lowe’s ratio test [1]}, and we further filter the matched feature descriptors by keeping only the pairs whose euclidean distance is below a given threshold. Finally, we obtain the homography transformation by applying the ransac [3]} algorithm to the filtered feature descriptors. fig:stichingexample shows the concatenation of two neighboring images \(i\)  and \(j\)  in which we apply the found homographic matrix to the image \(i\) , to have the same perspective as the image \(j\) .
